Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations178
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 KiB
Average record size in memory120.7 B

Variable types

Text1
Numeric13
Categorical1

Alerts

Alcohol is highly overall correlated with Class and 2 other fieldsHigh correlation
Class is highly overall correlated with Alcohol and 6 other fieldsHigh correlation
Color Intensity is highly overall correlated with Alcohol and 1 other fieldsHigh correlation
Flavonoids is highly overall correlated with Class and 5 other fieldsHigh correlation
Hue is highly overall correlated with Class and 2 other fieldsHigh correlation
Magnesium is highly overall correlated with ProlineHigh correlation
Malic Acid is highly overall correlated with HueHigh correlation
Non-Flavonoid Phenols is highly overall correlated with FlavonoidsHigh correlation
Phenol Ratio is highly overall correlated with Class and 3 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with Flavonoids and 2 other fieldsHigh correlation
Proline is highly overall correlated with Alcohol and 2 other fieldsHigh correlation
Total Phenols is highly overall correlated with Class and 3 other fieldsHigh correlation
Label has unique values Unique

Reproduction

Analysis started2025-03-05 13:30:58.727250
Analysis finished2025-03-05 13:31:08.632089
Duration9.9 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Label
Text

Unique 

Distinct178
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:08.736108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length42
Median length40
Mean length21.780899
Min length18

Characters and Unicode

Total characters3877
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)100.0%

Sample

1st rowFrappato Sicily 1982
2nd rowTaurasi Umbria 1989
3rd rowSagrantino Umbria 1994
4th rowMorellino Veneto 2015
5th rowVino Nobile di Montepulciano Umbria 2008
ValueCountFrequency (%)
campania 30
 
5.4%
veneto 25
 
4.5%
umbria 24
 
4.3%
sicily 22
 
3.9%
abruzzo 21
 
3.8%
tuscany 21
 
3.8%
lombardy 18
 
3.2%
piedmont 17
 
3.0%
barolo 12
 
2.2%
montepulciano 9
 
1.6%
Other values (73) 359
64.3%
2025-03-05T14:31:08.903528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
380
 
9.8%
a 347
 
9.0%
o 290
 
7.5%
i 240
 
6.2%
n 216
 
5.6%
e 200
 
5.2%
r 190
 
4.9%
0 161
 
4.2%
1 141
 
3.6%
9 135
 
3.5%
Other values (38) 1577
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
380
 
9.8%
a 347
 
9.0%
o 290
 
7.5%
i 240
 
6.2%
n 216
 
5.6%
e 200
 
5.2%
r 190
 
4.9%
0 161
 
4.2%
1 141
 
3.6%
9 135
 
3.5%
Other values (38) 1577
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
380
 
9.8%
a 347
 
9.0%
o 290
 
7.5%
i 240
 
6.2%
n 216
 
5.6%
e 200
 
5.2%
r 190
 
4.9%
0 161
 
4.2%
1 141
 
3.6%
9 135
 
3.5%
Other values (38) 1577
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
380
 
9.8%
a 347
 
9.0%
o 290
 
7.5%
i 240
 
6.2%
n 216
 
5.6%
e 200
 
5.2%
r 190
 
4.9%
0 161
 
4.2%
1 141
 
3.6%
9 135
 
3.5%
Other values (38) 1577
40.7%

Alcohol
Real number (ℝ)

High correlation 

Distinct126
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.000618
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:08.971420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.6585
Q112.3625
median13.05
Q313.6775
95-th percentile14.2215
Maximum14.83
Range3.8
Interquartile range (IQR)1.315

Descriptive statistics

Standard deviation0.81182654
Coefficient of variation (CV)0.062445227
Kurtosis-0.85249957
Mean13.000618
Median Absolute Deviation (MAD)0.68
Skewness-0.051482331
Sum2314.11
Variance0.65906233
MonotonicityNot monotonic
2025-03-05T14:31:09.046524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.37 6
 
3.4%
13.05 6
 
3.4%
12.08 5
 
2.8%
12.29 4
 
2.2%
12.42 3
 
1.7%
12 3
 
1.7%
12.25 3
 
1.7%
13.5 2
 
1.1%
13.16 2
 
1.1%
13.58 2
 
1.1%
Other values (116) 142
79.8%
ValueCountFrequency (%)
11.03 1
0.6%
11.41 1
0.6%
11.45 1
0.6%
11.46 1
0.6%
11.56 1
0.6%
11.61 1
0.6%
11.62 1
0.6%
11.64 1
0.6%
11.65 1
0.6%
11.66 1
0.6%
ValueCountFrequency (%)
14.83 1
0.6%
14.75 1
0.6%
14.39 1
0.6%
14.38 2
1.1%
14.37 1
0.6%
14.34 1
0.6%
14.3 1
0.6%
14.23 1
0.6%
14.22 2
1.1%
14.21 1
0.6%

Malic Acid
Real number (ℝ)

High correlation 

Distinct133
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3363483
Minimum0.74
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:09.123506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile1.061
Q11.6025
median1.865
Q33.0825
95-th percentile4.4555
Maximum5.8
Range5.06
Interquartile range (IQR)1.48

Descriptive statistics

Standard deviation1.1171461
Coefficient of variation (CV)0.47815905
Kurtosis0.29920668
Mean2.3363483
Median Absolute Deviation (MAD)0.52
Skewness1.0396512
Sum415.87
Variance1.2480154
MonotonicityNot monotonic
2025-03-05T14:31:09.196904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.73 7
 
3.9%
1.81 4
 
2.2%
1.67 4
 
2.2%
1.35 3
 
1.7%
1.61 3
 
1.7%
1.51 3
 
1.7%
1.68 3
 
1.7%
1.9 3
 
1.7%
1.53 3
 
1.7%
1.5 2
 
1.1%
Other values (123) 143
80.3%
ValueCountFrequency (%)
0.74 1
0.6%
0.89 1
0.6%
0.9 1
0.6%
0.92 1
0.6%
0.94 2
1.1%
0.98 1
0.6%
0.99 1
0.6%
1.01 1
0.6%
1.07 1
0.6%
1.09 1
0.6%
ValueCountFrequency (%)
5.8 1
0.6%
5.65 1
0.6%
5.51 1
0.6%
5.19 1
0.6%
5.04 1
0.6%
4.95 1
0.6%
4.72 1
0.6%
4.61 1
0.6%
4.6 1
0.6%
4.43 1
0.6%

Ash Content
Real number (ℝ)

Distinct79
Distinct (%)44.6%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.3668927
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:09.265785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.92
Q12.21
median2.36
Q32.56
95-th percentile2.742
Maximum3.23
Range1.87
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.27507634
Coefficient of variation (CV)0.11621834
Kurtosis1.124547
Mean2.3668927
Median Absolute Deviation (MAD)0.16
Skewness-0.18036319
Sum418.94
Variance0.075666994
MonotonicityNot monotonic
2025-03-05T14:31:09.343866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28 7
 
3.9%
2.7 6
 
3.4%
2.3 6
 
3.4%
2.32 6
 
3.4%
2.36 6
 
3.4%
2.48 5
 
2.8%
2.38 5
 
2.8%
2.2 5
 
2.8%
2.1 4
 
2.2%
2.4 4
 
2.2%
Other values (69) 123
69.1%
ValueCountFrequency (%)
1.36 1
 
0.6%
1.7 2
1.1%
1.71 1
 
0.6%
1.75 1
 
0.6%
1.82 1
 
0.6%
1.88 1
 
0.6%
1.9 1
 
0.6%
1.92 3
1.7%
1.94 1
 
0.6%
1.95 1
 
0.6%
ValueCountFrequency (%)
3.23 1
0.6%
3.22 1
0.6%
2.92 1
0.6%
2.87 1
0.6%
2.86 1
0.6%
2.84 1
0.6%
2.8 1
0.6%
2.78 1
0.6%
2.75 1
0.6%
2.74 2
1.1%

Ash Alkalinity
Real number (ℝ)

Distinct63
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.494944
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:09.418880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.77
Q117.2
median19.5
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.3395638
Coefficient of variation (CV)0.1713041
Kurtosis0.48794154
Mean19.494944
Median Absolute Deviation (MAD)2.05
Skewness0.21304689
Sum3470.1
Variance11.152686
MonotonicityNot monotonic
2025-03-05T14:31:09.499782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 15
 
8.4%
21 11
 
6.2%
16 11
 
6.2%
18 10
 
5.6%
19 9
 
5.1%
21.5 8
 
4.5%
19.5 7
 
3.9%
22 7
 
3.9%
18.5 7
 
3.9%
22.5 7
 
3.9%
Other values (53) 86
48.3%
ValueCountFrequency (%)
10.6 1
0.6%
11.2 1
0.6%
11.4 1
0.6%
12 1
0.6%
12.4 1
0.6%
13.2 1
0.6%
14 2
1.1%
14.6 1
0.6%
14.8 1
0.6%
15 2
1.1%
ValueCountFrequency (%)
30 1
 
0.6%
28.5 2
 
1.1%
27 1
 
0.6%
26.5 1
 
0.6%
26 1
 
0.6%
25.5 1
 
0.6%
25 5
2.8%
24.5 3
1.7%
24 5
2.8%
23.6 1
 
0.6%

Magnesium
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.741573
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:09.759101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile80.85
Q188
median98
Q3107
95-th percentile124.3
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.282484
Coefficient of variation (CV)0.14319489
Kurtosis2.1049913
Mean99.741573
Median Absolute Deviation (MAD)10
Skewness1.0981911
Sum17754
Variance203.98934
MonotonicityNot monotonic
2025-03-05T14:31:09.835630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 13
 
7.3%
86 11
 
6.2%
98 9
 
5.1%
101 9
 
5.1%
96 8
 
4.5%
102 7
 
3.9%
112 6
 
3.4%
94 6
 
3.4%
85 6
 
3.4%
89 5
 
2.8%
Other values (43) 98
55.1%
ValueCountFrequency (%)
70 1
 
0.6%
78 3
 
1.7%
80 5
 
2.8%
81 1
 
0.6%
82 1
 
0.6%
84 3
 
1.7%
85 6
3.4%
86 11
6.2%
87 3
 
1.7%
88 13
7.3%
ValueCountFrequency (%)
162 1
0.6%
151 1
0.6%
139 1
0.6%
136 1
0.6%
134 1
0.6%
132 1
0.6%
128 1
0.6%
127 1
0.6%
126 1
0.6%
124 1
0.6%

Total Phenols
Real number (ℝ)

High correlation 

Distinct97
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2951124
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:09.910615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.7425
median2.355
Q32.8
95-th percentile3.2745
Maximum3.88
Range2.9
Interquartile range (IQR)1.0575

Descriptive statistics

Standard deviation0.62585105
Coefficient of variation (CV)0.27268863
Kurtosis-0.83562652
Mean2.2951124
Median Absolute Deviation (MAD)0.505
Skewness0.086638586
Sum408.53
Variance0.39168954
MonotonicityNot monotonic
2025-03-05T14:31:09.986619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 8
 
4.5%
2.6 6
 
3.4%
2.8 6
 
3.4%
3 6
 
3.4%
2 5
 
2.8%
2.95 5
 
2.8%
1.65 4
 
2.2%
2.85 4
 
2.2%
2.45 4
 
2.2%
1.38 4
 
2.2%
Other values (87) 126
70.8%
ValueCountFrequency (%)
0.98 1
 
0.6%
1.1 1
 
0.6%
1.15 1
 
0.6%
1.25 1
 
0.6%
1.28 1
 
0.6%
1.3 1
 
0.6%
1.35 1
 
0.6%
1.38 4
2.2%
1.39 2
1.1%
1.4 2
1.1%
ValueCountFrequency (%)
3.88 1
 
0.6%
3.85 1
 
0.6%
3.52 1
 
0.6%
3.5 1
 
0.6%
3.4 1
 
0.6%
3.38 1
 
0.6%
3.3 3
1.7%
3.27 1
 
0.6%
3.25 2
1.1%
3.2 1
 
0.6%

Flavonoids
Real number (ℝ)

High correlation 

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0292697
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:10.059705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5455
Q11.205
median2.135
Q32.875
95-th percentile3.4975
Maximum5.08
Range4.74
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation0.99885869
Coefficient of variation (CV)0.4922257
Kurtosis-0.88038155
Mean2.0292697
Median Absolute Deviation (MAD)0.835
Skewness0.025343553
Sum361.21
Variance0.99771867
MonotonicityNot monotonic
2025-03-05T14:31:10.129316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.65 4
 
2.2%
0.6 3
 
1.7%
1.25 3
 
1.7%
2.68 3
 
1.7%
0.58 3
 
1.7%
2.03 3
 
1.7%
3.03 2
 
1.1%
0.5 2
 
1.1%
2.98 2
 
1.1%
3.15 2
 
1.1%
Other values (122) 151
84.8%
ValueCountFrequency (%)
0.34 1
0.6%
0.47 2
1.1%
0.48 1
0.6%
0.49 1
0.6%
0.5 2
1.1%
0.51 1
0.6%
0.52 1
0.6%
0.55 1
0.6%
0.56 1
0.6%
0.57 1
0.6%
ValueCountFrequency (%)
5.08 1
0.6%
3.93 1
0.6%
3.75 1
0.6%
3.74 1
0.6%
3.69 1
0.6%
3.67 1
0.6%
3.64 1
0.6%
3.56 1
0.6%
3.54 1
0.6%
3.49 1
0.6%

Non-Flavonoid Phenols
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36185393
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:10.195210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.27
median0.34
Q30.4375
95-th percentile0.6
Maximum0.66
Range0.53
Interquartile range (IQR)0.1675

Descriptive statistics

Standard deviation0.12445334
Coefficient of variation (CV)0.34393253
Kurtosis-0.63719106
Mean0.36185393
Median Absolute Deviation (MAD)0.085
Skewness0.45015134
Sum64.41
Variance0.015488634
MonotonicityNot monotonic
2025-03-05T14:31:10.265297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.43 11
 
6.2%
0.26 11
 
6.2%
0.29 10
 
5.6%
0.32 9
 
5.1%
0.37 8
 
4.5%
0.27 8
 
4.5%
0.3 8
 
4.5%
0.34 8
 
4.5%
0.4 8
 
4.5%
0.53 7
 
3.9%
Other values (29) 90
50.6%
ValueCountFrequency (%)
0.13 1
 
0.6%
0.14 2
 
1.1%
0.17 5
2.8%
0.19 2
 
1.1%
0.2 2
 
1.1%
0.21 6
3.4%
0.22 6
3.4%
0.24 7
3.9%
0.25 2
 
1.1%
0.26 11
6.2%
ValueCountFrequency (%)
0.66 1
 
0.6%
0.63 4
2.2%
0.61 3
1.7%
0.6 3
1.7%
0.58 3
1.7%
0.56 1
 
0.6%
0.55 1
 
0.6%
0.53 7
3.9%
0.52 5
2.8%
0.5 5
2.8%

Proanthocyanins
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5908989
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:10.330298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.555
Q31.95
95-th percentile2.709
Maximum3.58
Range3.17
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.57235886
Coefficient of variation (CV)0.35977074
Kurtosis0.55464852
Mean1.5908989
Median Absolute Deviation (MAD)0.38
Skewness0.51713717
Sum283.18
Variance0.32759467
MonotonicityNot monotonic
2025-03-05T14:31:10.406320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.35 9
 
5.1%
1.46 7
 
3.9%
1.87 6
 
3.4%
1.25 5
 
2.8%
1.66 4
 
2.2%
2.08 4
 
2.2%
1.98 4
 
2.2%
1.56 4
 
2.2%
1.95 3
 
1.7%
1.4 3
 
1.7%
Other values (91) 129
72.5%
ValueCountFrequency (%)
0.41 1
0.6%
0.42 2
1.1%
0.55 1
0.6%
0.62 1
0.6%
0.64 2
1.1%
0.68 1
0.6%
0.73 2
1.1%
0.75 1
0.6%
0.8 2
1.1%
0.81 1
0.6%
ValueCountFrequency (%)
3.58 1
 
0.6%
3.28 1
 
0.6%
2.96 1
 
0.6%
2.91 2
1.1%
2.81 3
1.7%
2.76 1
 
0.6%
2.7 1
 
0.6%
2.5 1
 
0.6%
2.49 1
 
0.6%
2.45 1
 
0.6%

Color Intensity
Real number (ℝ)

High correlation 

Distinct132
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0580899
Minimum1.28
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:10.481208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile2.114
Q13.22
median4.69
Q36.2
95-th percentile9.598
Maximum13
Range11.72
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.3182859
Coefficient of variation (CV)0.45833228
Kurtosis0.38152227
Mean5.0580899
Median Absolute Deviation (MAD)1.51
Skewness0.86858479
Sum900.34
Variance5.3744494
MonotonicityNot monotonic
2025-03-05T14:31:10.551210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 4
 
2.2%
2.6 4
 
2.2%
3.8 4
 
2.2%
5.1 3
 
1.7%
5.4 3
 
1.7%
5.7 3
 
1.7%
5.6 3
 
1.7%
3.4 3
 
1.7%
5 3
 
1.7%
4.5 3
 
1.7%
Other values (122) 145
81.5%
ValueCountFrequency (%)
1.28 1
0.6%
1.74 1
0.6%
1.9 1
0.6%
1.95 2
1.1%
2 1
0.6%
2.06 2
1.1%
2.08 1
0.6%
2.12 1
0.6%
2.15 1
0.6%
2.2 1
0.6%
ValueCountFrequency (%)
13 1
0.6%
11.75 1
0.6%
10.8 1
0.6%
10.68 1
0.6%
10.52 1
0.6%
10.26 1
0.6%
10.2 1
0.6%
9.899999 1
0.6%
9.7 1
0.6%
9.58 1
0.6%

Hue
Real number (ℝ)

High correlation 

Distinct78
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95744944
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:10.625210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7825
median0.965
Q31.12
95-th percentile1.2845
Maximum1.71
Range1.23
Interquartile range (IQR)0.3375

Descriptive statistics

Standard deviation0.22857157
Coefficient of variation (CV)0.23872965
Kurtosis-0.34409574
Mean0.95744944
Median Absolute Deviation (MAD)0.165
Skewness0.021091272
Sum170.426
Variance0.052244961
MonotonicityNot monotonic
2025-03-05T14:31:10.699301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.04 8
 
4.5%
1.23 7
 
3.9%
1.12 6
 
3.4%
0.57 5
 
2.8%
1.25 5
 
2.8%
0.96 5
 
2.8%
0.89 5
 
2.8%
1.05 4
 
2.2%
1.09 4
 
2.2%
0.86 4
 
2.2%
Other values (68) 125
70.2%
ValueCountFrequency (%)
0.48 1
 
0.6%
0.54 1
 
0.6%
0.55 1
 
0.6%
0.56 2
 
1.1%
0.57 5
2.8%
0.58 2
 
1.1%
0.59 2
 
1.1%
0.6 3
1.7%
0.61 2
 
1.1%
0.62 1
 
0.6%
ValueCountFrequency (%)
1.71 1
 
0.6%
1.45 1
 
0.6%
1.42 1
 
0.6%
1.38 1
 
0.6%
1.36 2
 
1.1%
1.33 1
 
0.6%
1.31 2
 
1.1%
1.28 2
 
1.1%
1.27 1
 
0.6%
1.25 5
2.8%

Phenol Ratio
Real number (ℝ)

High correlation 

Distinct122
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6116854
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:10.768208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4625
Q11.9375
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.2325

Descriptive statistics

Standard deviation0.70999043
Coefficient of variation (CV)0.27185144
Kurtosis-1.0864345
Mean2.6116854
Median Absolute Deviation (MAD)0.52
Skewness-0.3072855
Sum464.88
Variance0.50408641
MonotonicityNot monotonic
2025-03-05T14:31:10.837985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 5
 
2.8%
2.78 4
 
2.2%
1.82 4
 
2.2%
3 4
 
2.2%
3.33 3
 
1.7%
1.56 3
 
1.7%
2.77 3
 
1.7%
1.33 3
 
1.7%
3.17 3
 
1.7%
2.96 3
 
1.7%
Other values (112) 143
80.3%
ValueCountFrequency (%)
1.27 1
 
0.6%
1.29 2
1.1%
1.3 1
 
0.6%
1.33 3
1.7%
1.36 1
 
0.6%
1.42 1
 
0.6%
1.47 1
 
0.6%
1.48 1
 
0.6%
1.51 2
1.1%
1.55 1
 
0.6%
ValueCountFrequency (%)
4 1
0.6%
3.92 1
0.6%
3.82 1
0.6%
3.71 1
0.6%
3.69 1
0.6%
3.64 1
0.6%
3.63 1
0.6%
3.59 1
0.6%
3.58 2
1.1%
3.57 1
0.6%

Proline
Real number (ℝ)

High correlation 

Distinct121
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.89326
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-03-05T14:31:10.907985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.55
Q1500.5
median673.5
Q3985
95-th percentile1297.25
Maximum1680
Range1402
Interquartile range (IQR)484.5

Descriptive statistics

Standard deviation314.90747
Coefficient of variation (CV)0.42162313
Kurtosis-0.24840311
Mean746.89326
Median Absolute Deviation (MAD)202.5
Skewness0.76782178
Sum132947
Variance99166.717
MonotonicityNot monotonic
2025-03-05T14:31:10.986986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680 5
 
2.8%
520 5
 
2.8%
750 4
 
2.2%
625 4
 
2.2%
630 4
 
2.2%
1035 3
 
1.7%
450 3
 
1.7%
1285 3
 
1.7%
510 3
 
1.7%
495 3
 
1.7%
Other values (111) 141
79.2%
ValueCountFrequency (%)
278 1
0.6%
290 1
0.6%
312 1
0.6%
315 1
0.6%
325 1
0.6%
342 1
0.6%
345 2
1.1%
352 1
0.6%
355 1
0.6%
365 1
0.6%
ValueCountFrequency (%)
1680 1
0.6%
1547 1
0.6%
1515 1
0.6%
1510 1
0.6%
1480 1
0.6%
1450 1
0.6%
1375 1
0.6%
1320 1
0.6%
1310 1
0.6%
1295 1
0.6%

Class
Categorical

High correlation 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Barbaresco
71 
Barolo
59 
Barbera
48 

Length

Max length10
Median length7
Mean length7.8651685
Min length6

Characters and Unicode

Total characters1400
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBarolo
2nd rowBarolo
3rd rowBarbera
4th rowBarolo
5th rowBarbaresco

Common Values

ValueCountFrequency (%)
Barbaresco 71
39.9%
Barolo 59
33.1%
Barbera 48
27.0%

Length

2025-03-05T14:31:11.066078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-05T14:31:11.122061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
barbaresco 71
39.9%
barolo 59
33.1%
barbera 48
27.0%

Most occurring characters

ValueCountFrequency (%)
a 297
21.2%
r 297
21.2%
o 189
13.5%
B 178
12.7%
b 119
8.5%
e 119
8.5%
s 71
 
5.1%
c 71
 
5.1%
l 59
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 297
21.2%
r 297
21.2%
o 189
13.5%
B 178
12.7%
b 119
8.5%
e 119
8.5%
s 71
 
5.1%
c 71
 
5.1%
l 59
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 297
21.2%
r 297
21.2%
o 189
13.5%
B 178
12.7%
b 119
8.5%
e 119
8.5%
s 71
 
5.1%
c 71
 
5.1%
l 59
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 297
21.2%
r 297
21.2%
o 189
13.5%
B 178
12.7%
b 119
8.5%
e 119
8.5%
s 71
 
5.1%
c 71
 
5.1%
l 59
 
4.2%

Interactions

2025-03-05T14:31:07.759324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:58.981537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.839670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.514382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:01.356452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.088762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.825096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.521151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:04.267777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:04.936695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.622046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:06.281202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.099916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.816406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.067535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.899664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.659422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:01.417550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.243290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.881011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.572263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:04.324777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:04.990696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.676047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:06.336561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.153569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.866458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.133175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.950667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.714421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:01.473447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.291370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.932724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.616913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-05T14:31:05.043696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.726049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:06.385329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.203238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.924007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.193023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-05T14:31:01.532624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.343426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.005632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.665911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:04.426777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.097695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.778047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:06.438249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.254219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.979008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.253023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.061675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.830771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:01.588459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.398372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.067661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.717000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:04.480776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.154270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.830164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:06.652061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.309180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:08.029009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.383762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.109664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.882775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:01.640457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.444422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.114630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.762455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:04.527022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.202924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.879140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:06.697062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.356179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:08.081009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.437729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.159391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.939991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:01.696456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:02.493540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.166316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:03.813540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:04.576009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.254005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:05.934047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:06.749180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:07.411181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:08.129716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:30:59.487828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.206944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:00.990111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-05T14:31:01.753457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2025-03-05T14:31:11.177707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AlcoholAsh AlkalinityAsh ContentClassColor IntensityFlavonoidsHueMagnesiumMalic AcidNon-Flavonoid PhenolsPhenol RatioProanthocyaninsProlineTotal Phenols
Alcohol1.000-0.3070.2480.5810.6350.295-0.0240.3660.140-0.1620.1030.1930.6340.311
Ash Alkalinity-0.3071.0000.3670.382-0.074-0.444-0.353-0.1700.3040.389-0.326-0.254-0.456-0.377
Ash Content0.2480.3671.0000.2220.2850.082-0.0480.3640.2320.145-0.0060.0280.2580.136
Class0.5810.3820.2221.0000.6490.7520.5830.4030.4960.3550.6450.3990.6440.560
Color Intensity0.635-0.0740.2850.6491.000-0.043-0.4190.3570.2900.060-0.318-0.0310.4570.011
Flavonoids0.295-0.4440.0820.752-0.0431.0000.5350.233-0.325-0.5440.7420.7300.4300.879
Hue-0.024-0.353-0.0480.583-0.4190.5351.0000.036-0.560-0.2680.4850.3430.2080.439
Magnesium0.366-0.1700.3640.4030.3570.2330.0361.0000.080-0.2370.0570.1740.5080.246
Malic Acid0.1400.3040.2320.4960.290-0.325-0.5600.0801.0000.255-0.255-0.245-0.057-0.280
Non-Flavonoid Phenols-0.1620.3890.1450.3550.060-0.544-0.268-0.2370.2551.000-0.495-0.385-0.270-0.448
Phenol Ratio0.103-0.326-0.0060.645-0.3180.7420.4850.057-0.255-0.4951.0000.5540.2530.687
Proanthocyanins0.193-0.2540.0280.399-0.0310.7300.3430.174-0.245-0.3850.5541.0000.3080.667
Proline0.634-0.4560.2580.6440.4570.4300.2080.508-0.057-0.2700.2530.3081.0000.419
Total Phenols0.311-0.3770.1360.5600.0110.8790.4390.246-0.280-0.4480.6870.6670.4191.000

Missing values

2025-03-05T14:31:08.487109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-05T14:31:08.574177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

LabelAlcoholMalic AcidAsh ContentAsh AlkalinityMagnesiumTotal PhenolsFlavonoidsNon-Flavonoid PhenolsProanthocyaninsColor IntensityHuePhenol RatioProlineClass
0Frappato Sicily 198213.643.102.5615.21162.703.030.171.665.100.963.36845Barolo
1Taurasi Umbria 198914.214.042.4418.91112.852.650.301.255.240.873.331080Barolo
2Sagrantino Umbria 199412.932.812.7021.0961.540.500.530.754.600.772.31600Barbera
3Morellino Veneto 201513.731.502.7022.51013.003.250.292.385.701.192.711285Barolo
4Vino Nobile di Montepulciano Umbria 200812.371.171.9219.6782.112.000.271.044.681.123.48510Barbaresco
5Chianti Veneto 198514.301.922.7220.01202.803.140.331.976.201.072.651280Barolo
6Sangiovese Sicily 198012.003.432.0019.0872.001.640.371.871.280.933.05564Barbaresco
7Raboso Campania 200313.403.912.4823.01021.800.750.431.417.300.701.56750Barbera
8Frappato Umbria 202111.611.352.7020.0942.742.920.292.492.650.963.26680Barbaresco
9Perricone Abruzzo 198713.362.562.3520.0891.400.500.370.645.600.702.47780Barbera
LabelAlcoholMalic AcidAsh ContentAsh AlkalinityMagnesiumTotal PhenolsFlavonoidsNon-Flavonoid PhenolsProanthocyaninsColor IntensityHuePhenol RatioProlineClass
168Ghemme Tuscany 200611.651.672.6226.0881.921.610.401.342.601.363.21562Barbaresco
169Vino Nobile di Montepulciano Umbria 200211.961.092.3021.01013.382.140.131.653.210.993.13886Barbaresco
170Barbaresco Campania 198311.562.053.2328.51193.185.080.471.876.000.933.69465Barbaresco
171Nero d'Avola Tuscany 199314.134.102.7424.5962.050.760.561.359.200.611.60560Barbera
172Aglianico Campania 200914.061.632.2816.01263.003.170.242.105.651.093.71780Barolo
173Nero d'Avola Veneto 199013.861.512.6725.0862.952.860.211.873.381.363.16410Barbaresco
174Taurasi Abruzzo 202212.251.732.1219.0801.652.030.371.633.401.003.17510Barbaresco
175Brachetto Umbria 200514.381.872.3812.01023.303.640.292.967.501.203.001547Barolo
176Dolcetto Tuscany 198812.691.532.2620.7801.381.460.581.623.050.962.06495Barbaresco
177Lacrima Tuscany 199512.342.452.4621.0982.562.110.341.312.800.803.38438Barbaresco